Bibliographic record
Abstract
controllers, 118 Adaptive particle swarm optimization (APSO) algorithm, 411 Adaptive protection (AP), 297-298 AI. see Artificial intelligence (AI) AI Based Smart Energy Meter (AI-SEM), 301f AI-SEM.see AI Based Smart Energy Meter (AI-SEM) Alternative phase opposition disposition (APOD), 165 Amaris, Hortensia, 310 ANFIS.see Artificial neuro-fuzzy inference system (ANFIS) ANN.see Artificial neural network (ANN) AP. see Adaptive protection (AP) APOD.see Alternative phase opposition disposition (APOD) APSO algorithm.see Adaptive particle swarm optimization (APSO) algorithm ARMA.see Auto regressive moving average (ARMA) Artificial intelligence (AI), 80, 100t, 101, 293, 301 based smart energy meter (AI-SEM), 300-301 for smart grid (see smart grid) techniques, 290, 291-293, 393 Artificial neural network (ANN), 91-93, 92f, 291, 292, 292f, 295, 393 for captive power plant, 396-397 technique, 274 Artificial neuro-fuzzy inference system Boost converter output voltage, 173f BPA.see Back Propagation Algorithm (BPA) Branch current decomposition of loss allocation (BCDLA) method, 320, 327, 341 Build-Operate-Maintain-Transfer (BOMT) model design, 261 Canadian solar, 65 CPSO technique for, 69f, 70f proposed methods for, 71f three-diode model for, 65 Captive power plant, 394 ANN for, 396-397 Cascaded H-bridge (CHB) topology, 159-161 CBPWM techniques for MLI, 164-167 CEC-2005 benchmark functions, 10t, 18, 19-22t Centralised inverter configuration system, 259 Centralized configurations, 115 Centralized generation system, 298, 298f Central processing unit (CPU), 271 Chaotic particle swarm optimization (CPSO) for PV system modelling, 42 commercial solar cell models, 61 CPSO technique for Canadian solar, 69f, 70f non-parametric test outcomes, 53-60t, 72 one-dimensional chaotic maps, 42, 43t proposed methods for Canadian solar, 71f test problems with proposed methods, 46-52t three-diode model for Canadian solar, 65 three-diode model for Kyocera, 62t, 64t unimodal and multimodal test problems, 45t Charged EV, influence on number of, 426-427 Charge model, 185 Charging reservation scheduling stage, 415 Charging station (CS), 413, 417, 418, 426 CHB topology.see Cascaded H-bridge (CHB) topology Child nodes, 90-91 Circuit-based branch-oriented approach, 311, 341 Classical optimization methods, 3 Classifer methods, 88 Classifier island detection methods, 88f CM. see Common mode (CM) CMV.see Common mode voltage (CMV) Combined cycle power plant, 395-396, 395f Commercial solar cell models, 61 Common mode (CM), 122, 122f, 124f Common mode voltage (CMV), 117 Common static load models, 312 Communication architecture of HEMS, 294f Computational flow, 219-221 Computational time, 234 Constant current (CC), 311 Constant power (CP) load model, 311 Consumers type and their connected nodes, 319f Conventional energy resources, 294 Conventional grid versus smart grid, 289t Conventional power plants, 211 Conventional switching scheme, 122 Convergence characteristics, 234, 236f Converter controller, 374-
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.020 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".